tidy.summary_emm: Tidy a(n) summary_emm object

In broom: Convert Statistical Objects into Tidy Tibbles

Tidy a(n) summary_emm object

Description

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

Usage

## S3 method for class 'summary_emm'
tidy(x, null.value = NULL, ...)

Arguments

x	A summary_emm object.
null.value	Value to which estimate is compared.
...	Additional arguments passed to emmeans::summary.emmGrid() or lsmeans::summary.ref.grid(). Cautionary note: misspecified arguments may be silently ignored!

Details

Returns a data frame with one observation for each estimated marginal mean, and one column for each combination of factors. When the input is a contrast, each row will contain one estimated contrast.

There are a large number of arguments that can be passed on to emmeans::summary.emmGrid() or lsmeans::summary.ref.grid().

Value

A tibble::tibble() with columns:

conf.high	Upper bound on the confidence interval for the estimate.
conf.low	Lower bound on the confidence interval for the estimate.
contrast	Levels being compared.
den.df	Degrees of freedom of the denominator.
df	Degrees of freedom used by this term in the model.
null.value	Value to which the estimate is compared.
num.df	Degrees of freedom.
p.value	The two-sided p-value associated with the observed statistic.
std.error	The standard error of the regression term.
level1	One level of the factor being contrasted
level2	The other level of the factor being contrasted
term	Model term in joint tests
estimate	Expected marginal mean
statistic	T-ratio statistic or F-ratio statistic

See Also

tidy(), emmeans::ref_grid(), emmeans::emmeans(), emmeans::contrast()

Other emmeans tidiers: tidy.emmGrid(), tidy.lsmobj(), tidy.ref.grid()

Examples

# load libraries for models and data
library(emmeans)

# linear model for sales of oranges per day
oranges_lm1 <- lm(sales1 ~ price1 + price2 + day + store, data = oranges)

# reference grid; see vignette("basics", package = "emmeans")
oranges_rg1 <- ref_grid(oranges_lm1)
td <- tidy(oranges_rg1)
td

# marginal averages
marginal <- emmeans(oranges_rg1, "day")
tidy(marginal)

# contrasts
tidy(contrast(marginal))
tidy(contrast(marginal, method = "pairwise"))

# plot confidence intervals
library(ggplot2)

ggplot(tidy(marginal, conf.int = TRUE), aes(day, estimate)) +
  geom_point() +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high))

# by multiple prices
by_price <- emmeans(oranges_lm1, "day",
  by = "price2",
  at = list(
    price1 = 50, price2 = c(40, 60, 80),
    day = c("2", "3", "4")
  )
)

by_price

tidy(by_price)

ggplot(tidy(by_price, conf.int = TRUE), aes(price2, estimate, color = day)) +
  geom_line() +
  geom_errorbar(aes(ymin = conf.low, ymax = conf.high))

# joint_tests
tidy(joint_tests(oranges_lm1))

broom documentation built on Sept. 30, 2024, 9:29 a.m.